docstr: Return a tensor having the given shape and whose values are in the range [low, high)
docstr_tok: ["Return","a","tensor","having","the","given","shape","and","whose","values","are","in","the","range","[","low",",","high",")"]
code: def _get_samples(shape, low, high, device, dtype):
      """Return a tensor having the given shape and whose values are in the range [low, high)"""
      return (
          (high - low) * torch.rand(shape, device=device, dtype=dtype)
      ) + low
  
file_path: kornia/test/geometry/calibration/test_pnp.py
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  from kornia.geometry.calibration.pnp import _mean_isotropic_scale_normalize
  from kornia.testing import assert_close, tensor_to_gradcheck_var
label_desc: Use image node_id 1 for calling the TestSolvePnpDlt obj's underlying member method code with example usage: obj._get_samples(shape, low, high, device, dtype) and returns: unknown